Feature space reformation-based teacher-student architecture noise-tag-containing image classification method

A technology of feature space and classification method, applied in neural architecture, neural learning method, biological neural network model, etc., can solve the problem of ignoring the benefits of mutual learning, to promote self-learning, improve performance, robustness and effectiveness excellent effect

Pending Publication Date: 2022-03-11
匀熵教育科技(无锡)有限公司
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  • Feature space reformation-based teacher-student architecture noise-tag-containing image classification method
  • Feature space reformation-based teacher-student architecture noise-tag-containing image classification method
  • Feature space reformation-based teacher-student architecture noise-tag-containing image classification method

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[0027] 1, related work

[0028] 1.1 Noise Label

[0029]Deep neural networks can easily adapt to any noise due to its powerful learning ability. Training depth neural networks on noise data sets, making network fitting noise samples, greatly reducing the generalization of models. In order to overcome this problem, some researchers have modified the loss function to make the model more robust to noise. This method has great theoretical support, but with the increase in noise complexity, the effectiveness of the method gradually decreases. At the same time, the modification of the loss function increases the amount of calculation required for training convergence. Since the direct learning noise tag cannot achieve good results, the researchers turn to correct the noise label. Reed et al. [Reed, Scott, et al. * # * Training Deep Neural Networks on noisylabels with bootstrapping. * # * Arxiv Preprint Arxiv: 1412.6596 (2014).] Combined the original label with model prediction to genera...

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Abstract

The invention discloses a teacher-student architecture noise-label-containing image classification method based on feature space reforming, and belongs to the field of noise label learning and image classification. The success of deep learning mainly depends on a large-scale tagged data set. Most of data sets are manually labeled, so that the labeling accuracy is difficult to guarantee. Therefore, noise tags are inevitable in a real-world data set. Due to the strong learning ability of the deep neural network, the noise label is very easy to over-fit, and the model performance is reduced. In order to solve the problem, the invention provides a teacher-student structure based on feature space reformation, and the teacher-student structure is used for learning noise labels. A teacher model is trained to correct noise labels for student models. A feature space reforming mechanism is applied to guide a student model to carry out self-learning, so that the student model learns more meaningful information. We assess our methods on the three reference data sets. Experimental results verify the effectiveness of the proposed architecture.

Description

technical field [0001] The invention belongs to the field of noise label learning and image classification, and in particular relates to a method for classifying images with noise labels based on a teacher-student structure based on feature space renormalization. Background technique [0002] Deep learning has achieved remarkable success in many areas of computer vision, but its success is largely due to huge labeled datasets such as ImageNet. However, collecting such a large labeled dataset usually takes a lot of time and effort. Nowadays, most datasets are manually labeled, which leads to a problem that the ratio of noisy labels increases as the amount of data increases. Therefore, the accuracy of the labels in the dataset cannot be guaranteed. Studies have shown that deep neural networks are prone to overfitting noisy labels, resulting in weakened model performance. Therefore, how to train robust models in noisy environments has aroused great interest of researchers. ...

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Application Information

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IPC IPC(8): G06V10/764G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24
Inventor 孙俊
Owner 匀熵教育科技(无锡)有限公司
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